Method for predicting a user&#39;s future glycemic state

ABSTRACT

A method for predicting a user&#39;s future glycemic state includes measuring a user&#39;s glucose concentration at intervals over a time duration, thereby generating a plurality of glucose concentrations as a function of time. First and second glucose prediction equations that are fits to the plurality of glucose concentrations based on first and second non-identical mathematical models, respectively, are then derived. The method also includes calculating first and second predicted glucose concentrations at a future time using the first and second glucose prediction equations, respectively. Thereafter, an average predicted glucose concentration and a merit index are calculated based on the first and second predicted glucose calculations. The plurality of glucose concentrations as a function of time, the merit index and average predicted glucose concentration are input into a trained model (for example, a Hidden Markov Model) that outputs a set of glucose concentration probabilities. The user&#39;s future glycemic state is then predicted based on the set of glucose concentration probabilities.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates, in general, to medical devices and, inparticular, to glycemic state prediction devices, kits and associatedmethods.

2. Description of Related Art

Patients with diabetes can find it difficult to maintain their glucoseconcentration in a preferred euglycemic state (i.e., a glucoseconcentration the range of between about 70 mg/dL and 180 mg/dL, alsoreferred to as normoglycemic). As a consequence, such patients mayexperience deleterious hypoglycemic and hyperglycemic states. Reducingthe number of wide swings in glucose concentration is believed tosignificantly reduce the occurrence of long term diabetes-relatedcomplications such as blindness, kidney failure, retinopathy, and heartdisease.

Continuous glucose monitors (CGM's) have been developed to frequentlyand conveniently measure a patient's glucose concentration byautomatically collecting a large number of glucose concentrations overan extended time duration. Therefore, a patient using a CGM has theopportunity to dose themselves with relatively frequent insulininjections and, thereby, effectively regulate their glucoseconcentration.

However, a potential drawback of more frequent insulin injections is anincrease in the likelihood of the patient inadvertently entering ahypoglycemic state. Hypoglycemic states are dangerous because they canresult in a loss of consciousness and, in some instances, death.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity inthe appended claims. A better understanding of the features andadvantages of the present invention will be obtained by reference to thefollowing detailed description that sets forth illustrative embodiments,in which the principles of the invention are utilized, and theaccompanying drawings, in which like numerals indicate like elements, ofwhich:

FIG. 1 is a flow diagram depicting stages in process according to anexemplary embodiment of the present invention;

FIG. 2 is a Hidden Markov Model emission matrix table relevant to theprocess of FIG. 1;

FIG. 3 depicts an exemplary Hidden Markov Model (HMM) that shows aplurality of probabilities for transitioning between glycemic statesthat is relevant to the process of FIG. 1;

FIG. 4 is a simplified block diagram of a medical device for predictinga user's future glycemic state according to an embodiment of the presentinvention; and

FIG. 5 is a simplified block diagram of a kit for predicting a user'sfuture glycemic state according to an embodiment of the presentinvention.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

The following detailed description should be read with reference to thedrawings, in which like elements in different drawings are identicallynumbered. The drawings, which are not necessarily to scale, depictselected exemplary embodiments for the purpose of explanation only andare not intended to limit the scope of the invention. The detaileddescription illustrates by way of example, not by way of limitation, theprinciples of the invention. This description will clearly enable oneskilled in the art to make and use the invention, and describes severalembodiments, adaptations, variations, alternatives and uses of theinvention, including what is presently believed to be the best mode ofcarrying out the invention.

FIG. 1 is a flow diagram depicting stages in method 100 for predicting auser's future glycemic state according to an exemplary embodiment of thepresent invention. In this regard, one skilled in the art will recognizethat a user's future glycemic state can be generally categorized asbeing one of three possible states, i.e., hypoglycemia, euglycemia, orhyperglycemia. Methods according to embodiments of the present invention(including method 100) employ measurements and algorithms (for example,mathematical equations, mathematical models and statistical models) toassess the probability of a user entering one of these three states, orother glucose concentration ranges, at a future time.

At step 110 of method 100, a user's glucose concentration (e.g., bloodglucose concentration) is measured at intervals over a predeterminedtime duration, thereby generating a plurality of glucose concentrationsas a function of time (i.e., a series of glucose concentrations, witheach glucose concentration in the series being associated with a time atwhich that particular glucose concentration was measured).

The predetermined time duration can be, for example, in the range ofabout 10 minutes to about 30 minutes. The intervals can be eitherpredetermined or determined dynamically based on previous measurements.Typical, but non-limiting, predetermined intervals are in the range of0.2 minutes to 1 minute (in other words a single measurement is madeevery 0.2 minutes to every 1 minute during the predetermined timeduration).

Step 110 can be accomplished, for example, using any suitable continuousglucose monitor (CGM) known to one skilled in the art. A non-limitingexample of a CGM that can be employed in embodiments of the presentinvention is the Guardian® RT Continuous Monitoring System (commerciallyavailable from Medtronic MiniMed, Northridge, Calif. 91325-1219).

If desired, the plurality of glucose concentrations as a function oftime can be converted into a unitless plurality if glucoseconcentrations as a function of time by employing the followingequation:

$\begin{matrix}{{g\left( t_{i} \right)} = \frac{{G\left( t_{i} \right)} - {G\left( t_{\min} \right)}}{{G\left( t_{\max} \right)} - {G\left( t_{\min} \right)}}} & {{Eq}.\mspace{14mu} 1}\end{matrix}$

where:

-   -   t_(i)=a value of time during the pre-determined time duration;    -   t_(min)=the time when the glucose concentration was the smallest        in magnitude during the pre-determined time duration;    -   t_(max)=the time when the glucose concentration was the largest        in magnitude during the pre-determined time duration;    -   G(t_(i))=glucose concentration at time t_(i);

G(t_(min))=smallest magnitude glucose concentration;

G(t_(max))=largest magnitude glucose concentration;

g(t_(i))=unit glucose concentration at time t_(i); and

i=time interval variable incrementing between the start and the end ofthe predetermined time duration

Moreover, time t_(i) may be time shifted with respect to the initialtime t_(int) of the pre-determined time interval as follows:

t _(i shift) =t _(i) −t _(int)  Eq. 2

where:

t_(iShift)=a shifted time value; and

-   t_(int) is the first time value of the pre-determined time interval    that causes t_(0shift) to be equal to zero after implementing the    time shift using Eq. 2.

The unitless plurality of glucose concentration as a function of timeand the shifted times described above can, if desired, be employed insubsequent steps of process 100 in place of the herein describedplurality of glucose concentrations as a function of time to avoidcomplications during mathematical model fitting (for example, duringsteps 120 and 130 of method 100).

Subsequently, at step 120, a first glucose prediction equation that is afit to the plurality of glucose concentrations as a function of time isderived, with the fit being based on a first mathematical model. Forexample, the first glucose prediction equation may be a linear equationfit to the plurality of glucose concentration as a function of time.Such a first glucose prediction equation is illustrated by thefollowing:

g1(t)=at+b  Eq. 3

where:

g1(t) is the predicted glucose concentration at time t derived using thefirst glucose prediction equation;

-   -   a is a constant value that represents a slope, and    -   b is a constant value that represents an intercept.

One skilled in the art will recognize that the constants a and b can bederived from the plurality of glucose concentrations as a function oftime using any suitable mathematical modeling technique and, if desired,any suitable automated technique such as a combination of software and acomputer processor. Nonlimiting examples of suitable mathematicaltechniques include spline extrapolation, extrapolative smoothing, leastsquares, higher polynomial extrapolation, and Taylor series expansion.

At step 130, a second glucose prediction equation that is also a fit tothe plurality of glucose concentrations as a function of time is derivedwith the fit being based on a second mathematical model. It should benoted that the second mathematical model is not the same mathematicalmodel as was used in step 120. In other words, the first and secondmathematical models are non-identical to one another.

By employing both a first and a second glucose prediction equation, aquantitative merit index (described further herein) is readilycalculated. Although a quantitative merit index could theoretically becalculated based on just one glucose prediction equation, the predictivebenefit of a merit index is increased when more than one glucoseprediction equation is employed (with each of the glucose predictionequations being based on different mathematical models). Moreover, onceapprised of the present disclosure, one skilled in the art willrecognize that methods according to the present invention can employmore than two glucose prediction equations (for example, a third glucoseprediction equation).

For example, the second glucose prediction equation can be derived as afit to the plurality of glucose concentrations as a function of timebased on a quadratic equation. The second glucose prediction equationwould then take the following form:

g2(t)=ct ² +dt+e  Eq. 4

where:

-   -   g2(t) is the second predicted glucose concentration at time t.    -   c, d, e are constants, which are derived from the plurality of        glucose concentrations as a function of time.

It should be noted that embodiments of the invention are not limited toa linear or a quadratic equation for the derivation of the first andsecond glucose prediction equations and that other mathematical modelsmay be used to fit the plurality of glucose concentrations as a functionof time. For example, the first and second glucose prediction equationsequation may be based on mathematical models (and be in the form of) inthe form of a cubic equation (Eq. 5 below), one term power function (Eq.6 below), two term power function (Eq. 7), one term exponential function(Eq. 8), and a two term exponential function (Eq. 9).

g(t)=at ³ +bt ² +ct+d  Eq. 5

g(t)=at ^(b)  Eq. 6

g(t)=at ^(b) +c  Eq. 7

g(t)=ae ^(bt)  Eq. 8

g(t)=ae ^(bt) +ce ^(dt)  Eq. 9

where:

-   -   t=the variable time;    -   a, b, c and d represents constants of their respective        equations; and    -   e represents an exponential function.        However, for predetermined time intervals of approximately 30        minutes it is expected that glucose concentrations will        typically not experience more than one change of direction        (i.e., from falling to rising or the reverse). Therefore, the        use of cubic polynomial or higher equations/models may introduce        complexity and produce poor results when the plurality of        glucose concentration is noisy and/or contains unreliable data        points. Moreover, linear and quadratic equations/models have the        benefit of being robust when applied to noisy data. In addition,        the quadratic equation will accommodate a change in direction of        glucose concentration while a linear equation is compatible with        stagnant glucose concentrations. The use of a linear equation        and a quadratic equations/models (as described above),        therefore, provides for predictive robustness and accuracy.

A first predicted glucose concentration at a predetermined future timeis then calculated using the first glucose prediction equation (see step140 of FIG. 1). Given that this glucose concentration is associated witha future time (i.e., a time following the end of the measurementduration), its calculation can be considered an extrapolation. Thepredetermined future time point is referred to as t_(f) and the firstpredicted glucose concentration is g1(t_(f)).

A second predicted glucose concentration at the same predeterminedfuture time is then calculated using the second glucose predictionequation, as set forth in step 150 of FIG. 1. Given that this glucoseconcentration is associated with a future time (i.e., a time followingthe end of the measurement duration), its calculation can also beconsidered an extrapolation. The predetermined future time point isagain t_(f) and the second predicted glucose concentration is g2(t_(f)).

The predetermined future time point can be any suitable future timepoint. For example, the predetermined future time point can be in therange of about ten minutes to about thirty minutes following the end ofthe predetermined time duration during which measurements are made.Factors to consider when predetermining the future time are (i) thepercentage of false alarms generated by the method that are consideredacceptable for the user since such a may increase with an increase inthe future time point value and (ii) the effective response time of aCGM to changing glucose concentrations. A lower limit for thepredetermined future time point can, if desired, be greater than theeffective response time of the CGM.

An average predicted glucose concentration and a merit index based onthe first and second predicted glucose concentrations are calculated instep 160. For example, the first predicted glucose concentrationg1(t_(f)) and the second predicted glucose concentration g2(t_(f)) maybe averaged to form an average predicted glucose concentration.

Moreover, the merit index is calculated based on a degree of correlationbetween the first predicted glucose concentration and the secondpredicted glucose concentration. For example, a merit index M may becalculated based on a ratio of the first predicted glucose concentrationg1(t_(f)) and the second predicted glucose concentration g2(t_(f)). Forexample, the merit index M may be the absolute difference or percentdifference between the first predicted glucose concentration g1(t_(f))and the second predicted glucose concentration g2(t_(f)). Eqs. 10 and 11define one embodiment of a merit index M.

M=g2(t _(f))−g1(t _(f))/g2(t _(f)), if g2(t _(f))≧g1(t _(f))  Eq. 10

M=g1(t _(f))−g2(t _(f))/g1(t _(f)), if g2(t _(f))<g1(t _(f))  Eq. 11

The merit index essentially serves as a measure of confidence in thefirst and second predicted glucose concentrations. Therefore, anysuitable method known to those of skill in the art for calculating ameasure of confidence can be employed to calculate the merit index basedon the first and second predicted glucose concentrations.

Subsequently, at step 170, the plurality of glucose concentrations as afunction of time, the average predicted glucose concentration and themerit index are input into a trained model (such as a Hidden MarkovModel HMM, Artificial Neural Network (ANN) model or Bayesian model) thatoutputs a set of glucose concentration probabilities for thepredetermined future time. The set of glucose concentrationprobabilities can cover, for example, a range of glucose concentrationsfrom about 1 mg/dL to about 620 mg/dL. This set of probabilities is alsoreferred to as an emission matrix. FIG. 2 is an exemplary Hidden MarkovModel derived emission matrix table relevant to the process of FIG. 1that includes the probability of each glucose concentration occurring atthe predetermined future time point over a range of glucoseconcentration in increments of one mg/dL. The table of FIG. 2 is dividedinto a hypoglycemic range (1 mg/dL−70 mg/dL), an euglycemic range (71mg/dL −179 mg/dL), and a hyperglycemic range (180 mg/dL −621 mg/dL).

It has been determined that a HMM is especially well suited for glucoseconcentration (i.e., glycemic state) prediction because an emissionsequence that is output from a HMM can be used without knowledge of thestate sequence of the user's glycemic process. In other words, theprobability of a user achieving a particular glucose concentration(i.e., being in a particular glycemic state at a future time) can beestimated without knowledge of the actual cause of transition to thatglucose concentration. An HMM assumes that the plurality of glucoseconcentrations collected during the pre-determined time duration areindicative of an actual cause of a change in glucose concentration at afuture time point, although that cause is unknown or “hidden.” Thus, the“hidden” actual cause can be modeled indirectly by using the pluralityof glucose concentrations collected over the pre-determined time period.

One skilled in the art will recognize that a conventional HMM wouldemploy only a time series input (e.g., a plurality of glucoseconcentrations as a function if time) to output a complete set ofprobabilities for a future time. However, methods according toembodiments of the present invention also input a merit index and theaverage predicted glucose concentration into a trained model (such as anHMM model). These two inputs are employed to define the set of glucoseconcentration probabilities for the predetermined future time such thatthe set is centered about the average predicted glucose concentrationand covers a range that is function of the merit index. For example, therange can be increased when the merit index indicates less confidence inthe average predicted glucose concentration and the range can bedecreased when the merit index indicates more confidence in the averagepredicted glucose concentration.

Assuming, for example, that the average predicted glucose concentrationis 85 mg/dL, the set of glucose concentration probabilities is limitedsuch that the set is centered on 85 mg/dL and has a range that is afunction of the merit index. The range (R) can, for example, be setusing the following equation:

R=+/−(20%(M))  Eq. 12

Assuming for the sake of illustration that M is 1.0, then the rangewould be +/−20%, i.e., 85 mg/dL+/−17 mg/dL. The trained model would thenoutput a set glucose concentration probabilities for the predeterminedfuture time for glucose concentrations in the range of 85+/−17 mg/dL(i.e., essentially between 67 mg/dL and 103 mg/dL in the table of FIG.2).

Subsequently, in step 180 of method 100, the user's future glycemicstate at the predetermined future time is predicted based on the set ofglucose concentration probabilities. For example, a hypoglycemic statecan be defined as glucose concentrations in the range of 1 mg/dL to 80mg/dL. If desirable based on the merit index and the average predictedglucose concentration, the set of probabilities within this range can besummed together to form a summation that represents the probability thata user will be in a hypoglycemic state at the predetermined future time.If the summation is greater than a pre-determined threshold (forexample, a predetermined threshold of 50%), then a hypoglycemic alarmcould be triggered. Similarly, the hyperglycemic glucose concentrationrange may be used for predicting hyperglycemia.

Moreover, a prediction for any glucose concentration range (and glycemicstate subranges within that range) of interest can be made using thetechniques described immediately above. For example, returning to theassumption above that the average predicted glucose concentration is 85mg/dL, the merit index is 1.0 and Eq. 12 is employed, the following setof glucose concentration probabilities could be used to predict theuser's future glycemic state:

-   -   Hypoglycemic subrange (>67 to 70 mg/dL)=15.0    -   Euglycemic subrange (>70 through <103 mg/dL)=23.3    -   Hyperglycemic subrange (>180 mg/dL and out of range of        interest)=0        Normalized to sum to 100%, the probabilities are:    -   Hypoglycemic subrange (>67 to 70 mg/dL)=39.2%    -   Euglycemic subrange (>70 through <103 mg/dL)=60.8%    -   Hyperglycemic subrange (>180 mg/dL and out of range of        interest)=0%        Such normalized probabilities can be compared to predetermined        thresholds for purposes of alarm triggering.

One skilled in the art will recognize that a Hidden Markov Models is amathematical model for estimating a probability for a particular statebeing achieved in a stochastic process. Data (such as a plurality ofglucose concentrations as a function of time) are input into a HiddenMarkov Model to generate an output consisting of the probability foreach state that may be achieved. Hidden Markov Models suitable for usein embodiments of the present invention can be suitably trained (i.e.,created) using a plurality of diabetic subjects over a period of timewho are subject to everyday conditions. For example, about 7 or morepeople with diabetes may have their glucose concentration monitored forabout 8 days to about 9 days. The people with diabetes may be monitoredat a measurement rate of about 0.2 measurements per minute to about 1measurement per minute using a commercially available Guardian® RTContinuous Monitoring System (Medtronic MiniMed, Northridge, Calif.91325-1219).

For training the Hidden Markov Model, each person with diabetes may bemonitored using three Guardian® Continuous Monitors over the 8 to 9 dayperiod, with each monitor being used for about 72 hours. Although theGuardian® RT Continuous Monitoring System was used to the clinical dateused to train the Hidden Markov Model employed to generate FIG. 2 andFIG. 3, other suitable CGMs or methods may be employed to generate HMMsfor use in the present invention. The Hidden Markov Model of FIG. 3 wastrained using the collected glucose concentrations and employing the“Statistics Toolbox” in MATLAB (R2006b, MathWorks, Natick, Mass.01760-2098).

FIG. 3 depicts an exemplary Hidden Markov Model (HMM) that shows aplurality of probabilities for transitioning between three glycemicstates that is relevant to the process of FIG. 1. The three glycemicstates are hypoglycemic, euglycemic, and hyperglycemic. For each of thethree states, a probability is shown that represents the likelihood of aparticular state transitioning to another state. For instance FIG. 3illustrates that a user in a hyperglycemic state has a 97.26% chance ofremaining in the hyperglycemic state, a 2.74% chance of transitioning tothe euglycemic state, and a 0% chance of transitioning to thehypoglycemic state. A user in a euglycemic state has a 97.4% chance ofremaining in the euglycemic state, a 1.52% chance of transitioning tothe hyperglycemic state, and a 1.08% chance of transitioning to thehypoglycemic state. A user in a hypoglycemic state has a 92.91% chanceof remaining in the hypoglycemic state, a 7.09% chance of transitioningto the euglycemic state, and a 0% chance of transitioning to thehyperglycemic state. For every predetermined time duration, methodsaccording to embodiments of the present invention can employ an HMM tooutput an emission matrix that describes the probability of a usertransitioning to an array of possible glucose concentration (i.e.,possible glycemic states) at a predetermined future time point.

Embodiments of the present invention are beneficial in that a user'sfuture hypoglycemic state (or other glycemic state) can be accurately,quickly and repeatedly predicted based only on prior glucosemeasurements from the user. Furthermore, embodiments of the presentinvention are capable of predicting hypoglycemia in a robust manner thatis not significantly influenced by noise in the measured glucoseconcentration measurements due to the employment of a trained model(such as a Hidden Markov Model).

FIG. 4 is a simplified block diagram of a medical device 300 (within thedashed lines) for predicting a user's future glycemic state according toan embodiment of the present invention. Medical device 300 includes amemory module 310, a processor module 320 and a user alert module 330.The double-headed arrows of FIG. 4 indicate that each of the modules isin operative communication with the other modules be it by wiredtransmission, wireless transmission or other suitable means.

Memory module 310 is configured to receive and store a plurality ofglucose concentrations as a function of time that were generated by acontinuous glucose monitor. Memory module 310 can be any suitable memorymodule including memory modules that employ integrated circuits (e.g.,DRAM and SRAM based memory modules) and/or optical memory technologies.

Processor module 320 is configured to: (i) derive a first glucoseprediction equation that is a fit to the plurality of glucoseconcentrations as a function of time stored in the memory module, thefit being based on a first mathematical model; (ii) derive a secondglucose prediction equation that is a fit to the plurality of glucoseconcentrations as a function of time, the fit being based on a secondmathematical model; (iii) calculate a first predicted glucoseconcentration at a predetermined future time using the first glucoseprediction equation; (iv) calculate a second predicted glucoseconcentration at the predetermined future time using the second glucoseprediction equation; (v) calculate an average predicted glucoseconcentration and a merit index based on the first and second predictedglucose calculations; (vi) input the plurality of glucose concentrationsas a function of time, the average predicted glucose concentration andthe merit index into a trained model that outputs a set of glucoseconcentration probabilities for the predetermined future time; and (vii)predict user's future glycemic state based on the set of glucoseconcentration probabilities.

User alert module 330 is configured to alert the user in a mannerdependent on the glucose probabilities for the predetermined futuretime. User alert module 330 can be, for example, a visual display, anaudible alarm generation device, a tactile sensation generation deviceor any combination thereof.

Medical device 300 (and kit 400 described further below) implementmethods according to the present invention including, for example method100 described above. Therefore, characteristics, benefits, and aspectsof described with respect to methods according to the present inventioncan be incorporated into medical devices and kits of the presentinvention.

FIG. 5 is a simplified block diagram of a kit 400 for predicting auser's future glycemic state according to an exemplary embodiment of thepresent invention. Kit 400 includes a continuous glucose monitor 410 anda medical device 300. Medical device 300 was described above. The dasheddouble-headed arrow of FIG. 5 indicates that the continuous glucosemonitor is in operative communication with medical device 300 by, forexample, a suitable wired or wireless communication technique.

Continuous glucose monitor 410 is configured to measure a user's glucoseconcentration at intervals over a predetermined time duration, therebygenerating a plurality of glucose concentrations as a function of time.Continuous glucose monitoring 410 can be any suitable CGM known to oneskilled in the art.

While preferred embodiments of the present invention have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. Numerousvariations, changes, and substitutions will now occur to those skilledin the art without departing from the invention. It should be understoodthat various alternatives to the embodiments of the invention describedherein may be employed in practicing the invention. It is intended thatthe following claims define the scope of the invention and that devicesand methods within the scope of these claims and their equivalents becovered thereby.

1. A method for predicting a user's future glycemic state comprising:measuring a user's glucose concentration at intervals over apredetermined time duration, thereby generating a plurality of glucoseconcentrations as a function of time; deriving a first glucoseprediction equation that is a fit to the plurality of glucoseconcentrations as a function of time, the fit being based on a firstmathematical model; deriving a second glucose prediction equation thatis a fit to the plurality of glucose concentrations as a function oftime, the fit being based on a second mathematical model; calculating afirst predicted glucose concentration g1(t_(f)) at a predeterminedfuture time (t_(f)) using the first glucose prediction equation;calculating a second predicted glucose concentration g2(t_(f)) at thepredetermined future time (t_(f)) using the second glucose predictionequation; calculating an average predicted glucose concentration and amerit index M based on the first and second predicted glucosecalculations; inputting the plurality of glucose concentrations as afunction of time, the average predicted glucose concentration and themerit index into a trained model that outputs a set of glucoseconcentration probabilities, and predicting the user's future glycemicstate based on the set of glucose concentration probabilities, whereinthe first mathematical model and the second mathematical model arenon-identical to one another.
 2. The method of claim 1 wherein the meritindex is dependent on correlation between the first predicted glucoseconcentration and the second predicted glucose concentration.
 3. Themethod of claim 1 wherein the first mathematical model and the secondmathematical model are one of a linear model, a quadratic model, a oneterm power function model, a two term power function model, a one termexponential function model and a two term exponential function model. 4.The method of claim 1 wherein the trained model is a Hidden Markov Model(HMM).
 5. The method of claim 1 wherein the predicting step predicts theprobability that the user will be in a hypoglycemic state at thepredetermined future time based on the set of glucose concentrationprobabilities.
 6. The method of claim 5 further including the step of:altering the user if the probability of the user being in a hypoglycemicstate is greater than a predetermined threshold probability.
 7. Themethod of claim 1 wherein the plurality if glucose concentrations as afunction of time is in the range of seven to thirst glucoseconcentrations as a function of time.
 8. The method of claim 1, whereinthe predetermined future time point is a time in the range of about 10minutes to about 30 minutes after the predetermined time duration. 9.The method of claim 1 wherein the measuring step is accomplished using acontinuous glucose monitor.
 10. The method of claim 1 wherein theinterval is in the range of about 0.2 minutes to about 1 minute.
 11. Themethod of claim 1 wherein the merit index M is calculated using thefollowing algorithms:M=g2(t _(f))−g1(t _(f))/g2(t _(f)), if g2(t _(f))≧g1(t _(f))M=g1(t _(f))−g2(t _(f))/g1(t _(f)), if g2(t _(f))<g1(t _(f))
 12. Themethod of claim 1 wherein the first mathematical model is a linear modeland the second mathematical model is a quadratic model.
 13. The methodof claim 1 wherein the set of glucose concentration probabilities iscentered on the average predicted glucose concentration and has a rangethat is dependent on the merit index.